Open Source Fincept Terminal Alternatives

alphai-tui is a terminal dashboard built in Rust that brings together live price data and financial data tools that usually live in separate browser tabs. It's aimed at developers and traders who already live in the terminal and want market context without switching windows.

Quotes and candlestick charts work out of the box with no account. A free AlphAI key (no card required) unlocks the rest: AI-scored news, per-ticker sentiment, SEC Form 4 insider activity, and structured earnings reads.

Seven views, one keystroke apart:

• Split + Table is the default: watchlist with sparklines, a chart, and a news feed side by side. A quote rail runs across every view showing price, day range, and extended-hours prints.
• News feed shows scored articles (1-10 relevance) with category, sentiment, price impact, and a contrarian take per story. Covers a single ticker, the whole market, or a 48-hour trending top 10.
• Candlestick chart renders at half-block resolution with SMA 20/100 overlays, an RSI(14) panel, and a previous-close reference line. Intervals cycle on the fly.
• SEC Form 4 insider activity plots every filing event as a triangle on a log dollar scale, dims 10b5-1 plan trades, and shows weekly dollar bars, a 12-month rollup, and per-event stake impact.
• Earnings reads pull structured summaries from 8-K and 6-K filings: verdict, metric table with quarter-over-quarter and year-over-year changes, segments, outlook, and concerns.
• Summary grid tiles every ticker in your watchlist as a small chart sized to the terminal, so one glance covers the whole session.

The watchlist is editable while the app runs. Settings persist in a config file, so after the first launch a bare alphai-tui is enough.

It's a self-contained process, which makes it pair well with a terminal multiplexer. Run it alongside something like OpenBB or an LLM agent in adjacent panes, connecting the agent to the same AlphAI API the dashboard reads from. The free API plan allows 20 requests per minute and 100 per day; the app caches responses for five minutes and fetches only what the visible view needs, so it stays within that budget comfortably.

Prices default to Yahoo Finance, with Finnhub and Alpaca as alternatives for real-time quotes and full history. MIT licensed, runs on macOS, Linux, and Windows.

OpenBB is an agentic workspace designed for investment teams that need to bring fragmented data sources and AI tools together without building custom infrastructure from scratch. It targets asset managers, hedge funds, and banks that want production-ready analytics applications without months of engineering work.

The core idea is straightforward: your firm already has proprietary data, licensed feeds, and internal tools. OpenBB provides the layer that connects all of it, structured and unstructured alike, and makes it available to AI agents in a controlled, auditable environment. Think of it as an alternative to tools like Hebbia but built around a fully self-hostable, open-source foundation.

Key capabilities include:

• Data integration across proprietary, licensed, and public sources, both structured (market data, financials) and unstructured (filings, transcripts, emails)
• Bring your own agent support, so teams can plug in their own AI models rather than being locked into a single provider
• Customizable UI that adapts to specific workflows and supports team collaboration
• On-prem or private cloud deployment, with no data leaving your environment and no third-party model training on your positions
• SOC 2 Type II compliance, giving compliance teams the controls they need

Workflows span public equity research, private equity due diligence, credit risk, crypto analysis, commodities, and client advisory. Analysts can automate earnings intelligence extraction, generate investment notes from call transcripts, run portfolio stress tests, and draft investor letters, all from a single interface.

For teams already experimenting with AI agent frameworks or workflow automation, OpenBB provides the finance-specific context layer those tools lack. The workspace is designed for experimentation and production deployment simultaneously, so prototypes don't need to be rebuilt before going live.

Data privacy is a genuine differentiator here. AI models can run locally, prompts stay inside your environment, and your positions never touch an external training pipeline.